Online Deep Clustering for Unsupervised Representation Learning (2006.10645v1)

Abstract: Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature clustering and network parameters update leads to unstable learning of visual representations. To overcome this challenge, we propose Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly. Our key insight is that the cluster centroids should evolve steadily in keeping the classifier stably updated. Specifically, we design and maintain two dynamic memory modules, i.e., samples memory to store samples labels and features, and centroids memory for centroids evolution. We break down the abrupt global clustering into steady memory update and batch-wise label re-assignment. The process is integrated into network update iterations. In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly. Extensive experiments demonstrate that ODC stabilizes the training process and boosts the performance effectively. Code: https://github.com/open-mmlab/OpenSelfSup.

• The paper introduces ODC, integrating simultaneous clustering and network updates to enhance training stability.
• It demonstrates significant performance gains on benchmarks like ImageNet and Places205 compared to traditional deep clustering methods.
• ODC also acts as an effective unsupervised fine-tuning tool, boosting self-supervised models such as Jigsaw and Rotate.

Overview of Online Deep Clustering for Unsupervised Representation Learning

The paper "Online Deep Clustering for Unsupervised Representation Learning" introduces Online Deep Clustering (ODC), a novel approach aimed at stabilizing the training process in unsupervised representation learning. This work addresses the limitations of current deep clustering methods by integrating clustering and network updates in an online fashion, thereby enhancing training stability and representation efficacy.

Methodology and Innovations

Traditional deep clustering methods, such as Deep Clustering (DC), separate the clustering process from network updates, leading to instability due to abrupt changes in pseudo-labels. ODC mitigates this by performing clustering and updating network parameters simultaneously, allowing for a more stable learning trajectory.

Key to ODC's approach are two memory modules:

• Samples Memory: Stores samples' labels and features.
• Centroids Memory: Maintains evolving centroids for clustering.

Through these modules, ODC performs online, iterative updates, where labels evolve alongside network parameters, fostering continuous learning.

Experimental Results

The paper's results indicate that ODC substantially improves unsupervised learning performance across several benchmarks, including ImageNet and Places205, when utilizing deep architectures like ResNet-50. Noteworthy improvements are reported in the linear classification tasks, where ODC surpasses traditional DC in virtually all evaluations, particularly highlighting its robustness and scalability with deeper architectures.

ODC also demonstrates efficacy as an unsupervised fine-tuning tool that enhances the performance of existing self-supervised learning methods. Notable improvements were observed when fine-tuning models pre-trained with methods such as Jigsaw and Rotate.

Implications and Future Directions

The implications of ODC are profound, offering a more stable alternative to traditional unsupervised learning paradigms. Its ability to serve as a fine-tuning mechanism suggests flexibility and potential for broad adoption in various AI applications.

Future work may build upon this foundation by exploring:

• Extension to different architectures for even broader applicability.
• Hybrid models that combine ODC with supervised learning paradigms.
• Investigations into the theoretical aspects of clustering dynamics in online settings.

Overall, the introduction of ODC provides a significant step forward in unsupervised representation learning, offering both practical improvements in performance and a theoretical framework for more stable learning processes.